19 citations · 39 across the 15 of their papers we have counts for
5 papers · 1 filter
Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
Daniel Holmberg, Joel Oskarsson, Erik Wikingsson +2
Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilistic data-driven model for oc…
VIBE: Vector Index Benchmark for Embeddings
Elias Jääsaari, Ville Hyvönen, Matteo Ceccarello +2
Approximate nearest neighbor (ANN) search is a performance-critical component of many machine learning pipelines, and rigorous benchmarking is essential for assessing the performan…
LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor Search
Elias Jääsaari, Ville Hyvönen, Teemu Roos
Approximate nearest neighbor (ANN) search is a key component in many modern machine learning pipelines; recent use cases include retrieval-augmented generation (RAG) and vector dat…
Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures
Tomi Silander, Janne Leppä-aho, Elias Jääsaari +1
We introduce an information theoretic criterion for Bayesian network structure learning which we call quotient normalized maximum likelihood (qNML). In contrast to the closely rela…
Learning non-parametric Markov networks with mutual information
Janne Leppä-aho, Santeri Räisänen, Xiao Yang +1
We propose a method for learning Markov network structures for continuous data without invoking any assumptions about the distribution of the variables. The method makes use of pre…